A Robust Distributed Deep Learning Approach to Detect Alzheimer’s Disease from MRI Images

Tapotosh Ghosh, Md Istakiak Adnan Palash, Mohammad Abu Yousuf (), Md. Abdul Hamid, Muhammad Mostafa Monowar and Madini O. Alassafi
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Tapotosh Ghosh: Department of Computer Science and Engineering, United International University, Dhaka 1212, Bangladesh
Md Istakiak Adnan Palash: Department of Information and Communication Technology, Bangladesh University of Professionals, Dhaka 1216, Bangladesh
Mohammad Abu Yousuf: Institute of Information Technology, Jahangirnagar University, Savar 1342, Bangladesh
Md. Abdul Hamid: Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Muhammad Mostafa Monowar: Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Madini O. Alassafi: Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia

Mathematics, 2023, vol. 11, issue 12, 1-20

Abstract: Alzheimer’s disease has become a major concern in the healthcare domain as it is growing rapidly. Much research has been conducted to detect it from MRI images through various deep learning approaches.However, the problems of the availability of medical data and preserving the privacy of patients still exists. To mitigate this issue in Alzheimer’s disease detection, we implement the federated approach, which is found to be more efficient, robust, and consistent compared with the conventional approach. For this, we need deep excavation on various orientations of MRI images and transfer learning architectures. Then, we utilize two publicly available datasets (OASIS and ADNI) and design various cases to evaluate the performance of the federated approach. The federated approach achieves better accuracy and sensitivity compared with the conventional approaches in most of the cases. Moreover, the robustness of the proposed approach is also found to be better than the conventional approach. In our federated approach, MobileNet, a low-cost transfer learning architecture, achieves the highest 95.24%, 81.94%, and 83.97% accuracy in the OASIS, ADNI, and merged (ADNI + OASIS) test sets, which is much higher than the achieved performance in the conventional approach. Furthermore, in the proposed approach, only the weights of the model are shared, which keeps the original MRI images in their respective hospital or institutions, preserving privacy in the healthcare domain.

Keywords: federated learning; Alzheimer’s disease; medical imaging; MRI image (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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